Estimation of Uncertain Parameters in Single and Double Diode Models of Photovoltaic Panels Using Frilled Lizard Optimization

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Опубликовано в::Electronics vol. 14, no. 4 (2025), p. 796
Главный автор: Dal, Süleyman
Другие авторы: Sezgin, Necmettin
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MDPI AG
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100 1 |a Dal, Süleyman  |u Rectorate, Energy Coordination, Batman University, Batman 72000, Turkey 
245 1 |a Estimation of Uncertain Parameters in Single and Double Diode Models of Photovoltaic Panels Using Frilled Lizard Optimization 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Renewable energy sources are increasingly crucial for sustainable development. Photovoltaic (PV) systems, which convert solar energy into electricity, offer an environmentally friendly solution. Enhancing energy efficiency and minimizing environmental impacts in these systems heavily rely on parameter optimization. In this study, the Frilled Lizard Optimization (FLO) algorithm is proposed as a novel approach, integrating the newton-raphson method into the root mean square error (RMSE) objective function process to address nonlinear equations. Extensive analyses conducted on RTC France, STM6-40/36, and Photowatt PWP201 modules demonstrate the superior performance of the FLO algorithm using MATLAB R2022a software with Intel(R) Core(TM) i7-7500U CPU@ 2.70GHz 2.90 GHz 8 GB RAM. The RMSE values were calculated as 0.0030375 and 0.011538 for SDM and DDM in the RTC France dataset, 0.012036 for the STM6-40/36 dataset and 0.0097545 for the Photowatt-PWP201 dataset, respectively, indicating significantly lower error margins compared to other optimisation methods. Additionally, comprehensive evaluations were carried out using error metrics such as individual absolute error (IAE), relative error (RE) and mean absolute error (MAE), supported by detailed graphical representations of measured and predicted parameters. Current-voltage (I-V) and power-voltage (P-V) characteristic curves, as well as convergence behaviors, were systematically analyzed. This study introduces an innovative and robust solution for parameter optimization in PV systems, contributing to both theoretical and industrial applications. 
653 |a Accuracy 
653 |a Solar energy conversion 
653 |a Mathematical models 
653 |a Lizards 
653 |a Voltage 
653 |a Newton-Raphson method 
653 |a Numerical analysis 
653 |a Parameter robustness 
653 |a Energy resources 
653 |a Parameter uncertainty 
653 |a Photovoltaic cells 
653 |a Efficiency 
653 |a Datasets 
653 |a Electric potential 
653 |a Fossil fuels 
653 |a Root-mean-square errors 
653 |a Renewable energy sources 
653 |a Renewable resources 
653 |a Optimization 
653 |a Industrial applications 
653 |a Algorithms 
653 |a Sustainable development 
653 |a Methods 
653 |a Alternative energy sources 
653 |a Graphical representations 
653 |a Nonlinear equations 
653 |a Optimization algorithms 
653 |a Parameter estimation 
700 1 |a Sezgin, Necmettin  |u Department of Computer Engineering, Faculty of Engineering, Batman University, Batman 7200, Turkey; <email>necmettin.sezgin@batman.edu.tr</email> 
773 0 |t Electronics  |g vol. 14, no. 4 (2025), p. 796 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3171004695/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3171004695/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3171004695/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch